山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 57-63.doi: 10.6040/j.issn.1672-3961.0.2017.210
褚振忠,朱大奇
CHU Zhenzhong, ZHU Daqi
摘要: 研究自主式水下机器人(autonomous underwater vehicle, AUV)的推进器自适应区域跟踪容错控制方法。 与传统的自主式水下机器人容错控制方法不同,采用区域跟踪控制思想,将控制目标设定为以期望轨迹为中心的空间区域。 针对系统中存在的不确定性及推进器故障问题,采用神经网络进行在线辨识。 考虑到推进器故障时存在推力饱和而导致神经网络学习发散的问题,提出一种包含饱和因子的神经网络权值调整方法。 通过仿真,对所提方法的有效性进行验证。
中图分类号:
| [1] 朱大奇, 刘乾, 胡震. 无人水下机器人可靠性控制技术[J]. 中国造船,2009,50(2):183-192. ZHU Daqi, LIU Qian, HU Zhen. Reliability control technology of unmanned underwater vehicles[J]. Shipbuilding of China, 2009, 50(2):183-192. [2] CORRADINI M L, CRISTOFARO A. A nonlinear fault-tolerant thruster allocation architecture for underwater remotely operated vehicles[J]. IFAC-PapersOnLine, 2016, 49(23):285-290. [3] WANG Y, ZHANG M, WILSON P, et al. Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thrust fault[J]. Ocean Engieering, 2015, 110(1):15-24. [4] ZHANG M, LIU X, YIN B, et al. Adaptive terminal sliding mode based thruster fault tolerant control for underwater vehicle in time-varying ocean currents[J]. Journal of the Franklin Institute, 2015, 352(11):4935-4961. [5] ISMAIL Z H, MOKHAR M B M, PUTRANTI V W E, et al. A robust dynamic region-based control scheme for an autonomous underwater vehicle[J]. Ocean Engineering, 2016, 111:155-165. [6] CHEAH C C, WANG D Q. Region reaching control of robots: theory and experiments[C] // Proceedings of the 2005 IEEE International Conference on Robotics and Automation. [s.l.] :IEEE, 2005:974-979. [7] LI X, HOU S P, CHEAH C C. Adaptive region tracking control for autonomous underwater vehicle[C] // Proceedings of the 2010 11th International Conference on Control. Automation Robotics & Vision. Singapore: IEEE, 2010:2129-2134. [8] ISMAIL Z H, DUNNIGAN M W. A region boundary-based control scheme for an autonomous underwater vehicle[J]. Ocean Engineering, 2011, 38(11):2270-2280. [9] CORRADINI M L, MONTERIU A, ORLANDO G. An actuator failure tolerant control scheme for an underwater remotely operated vehicle[J]. IEEE Transactions on Control Systems Technology, 2011, 19(5):1036-1046. [10] KIM D W. Tracking of REMUS autonomous underwater vehicles with actuator saturations[J]. Automatica, 2015, 58:15-21. [11] GAO J, PROCTOR A A, SHI Y, et al. Hierarchical model predictive image-based visual serving of underwater vehicles with adaptive neural network dynamic control[J]. IEEE Transactions on Cybernetics, 2016, 46(10):2323-2334. [12] 俞建成, 张艾群, 王晓辉,等. 基于模糊神经网络水下机器人直接自适应控制[J]. 自动化学报, 2007, 33(8):840-846. YU Jiancheng, ZHANG Aiqun, WANG Xiaohui, et al. Direct adaptive control of underwater vehicles based on fuzzy neural networks[J]. Acta Automatica Sinica, 2007, 33(8):840-846. [13] SUN Y S, RAN X R, LI Y M, et al. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles[J]. International Journal of Naval Architecture and Ocean Engineering, 2016, 8(3):243-251. [14] 张铭钧, 褚振忠. 自主式水下机器人自适应区域跟踪控制[J]. 机械工程学院, 2013, 4(7):148-155. ZHANG Mingjun, CHU Zhenzhong. Adaptive region tracking control for autonomous underwater vehicle[J]. Journal of Mechanical Engineering, 2013, 4(7):148-155. [15] HUANG X, YAN Y, ZHOU Y. Neural network-based adaptive second order sliding mode control of Lorentz-augmented spacecraft formation[J]. Neurocopution, 2017, 222(26):191-203. [16] JIA C, LI X, WANG K, et al. Adaptive control of nonlinear system using online error minimum neural networks[J]. ISA Transactions, 2016, 65:125-132. [17] PODDER T K, SARKAR N. Fault-tolerant control of an autonomous underwater vehicle under thruster redundancy[J]. Robotics and Autonomous Systems, 2001, 34(1):39-52. |
| [1] | 周前,李群,朱丹丹,李仪博. 基于M3C自适应虚拟惯量的海上低频风电系统协调惯量响应控制[J]. 山东大学学报 (工学版), 2025, 55(5): 30-39. |
| [2] | 李晓辉,刘小飞,孙炜桐,赵毅,董媛,靳引利. 基于车辆与无人机协同的巡检任务分配与路径规划算法[J]. 山东大学学报 (工学版), 2025, 55(5): 101-109. |
| [3] | 郑晓,陈鹤,周东傲,宫永顺. 基于视频描述增强和双流特征融合的视频异常检测方法[J]. 山东大学学报 (工学版), 2025, 55(5): 110-119. |
| [4] | 高君健,廖祝华,刘毅志,赵肄江. 基于分层多智能体强化学习的个性化与信号控制联合路径引导方法[J]. 山东大学学报 (工学版), 2025, 55(3): 34-45. |
| [5] | 吴正健,吾尔尼沙·买买提,杨耀威,阿力木江·艾沙,库尔班·吾布力. 基于DRCoALTP的印刷体文档图像多文种识别方法[J]. 山东大学学报 (工学版), 2025, 55(1): 51-57. |
| [6] | 张梦雨,何振学,赵晓君,王浩然,肖利民,王翔. 基于AMSChOA的MPRM电路面积优化[J]. 山东大学学报 (工学版), 2024, 54(6): 147-155. |
| [7] | 王辰龑,刘轩,超木日力格. 自适应的并行天牛须优化算法[J]. 山东大学学报 (工学版), 2024, 54(5): 74-80. |
| [8] | 方世超,滕旭阳,王子南,陈晗,仇兆炀,毕美华. 基于自适应掩码和生成式修复的图像隐私保护技术[J]. 山东大学学报 (工学版), 2024, 54(5): 111-121. |
| [9] | 刘子一,崔超然,孟凡安,林培光. 基于批归一化统计量的无源多领域自适应方法[J]. 山东大学学报 (工学版), 2023, 53(2): 102-108. |
| [10] | 刘丁菠,刘学艳,于东然,杨博,李伟. 面向小样本目标检测任务的自适应特征重构算法[J]. 山东大学学报 (工学版), 2022, 52(6): 115-122. |
| [11] | 武新章,梁祥宇,朱虹谕,张冬冬. 基于CEEMDAN-GRA-PCC-ATCN的短期风电功率预测[J]. 山东大学学报 (工学版), 2022, 52(6): 146-156. |
| [12] | 许传臻,袭肖明,李维翠,孙仪,杨璐. 基于自适应多分辨率特征学习的CNV分型网络[J]. 山东大学学报 (工学版), 2022, 52(4): 69-75. |
| [13] | 孟祥飞,张强,胡宴才,张燕,杨仁明. 欠驱动船舶自适应神经网络有限时间跟踪控制[J]. 山东大学学报 (工学版), 2022, 52(4): 214-226. |
| [14] | 程业超,刘惊雷. 自适应图正则的单步子空间聚类[J]. 山东大学学报 (工学版), 2022, 52(2): 57-66. |
| [15] | 闵海根,方煜坤,吴霞,王武祺. 网联交通环境下的车-车通信故障诊断方法[J]. 山东大学学报 (工学版), 2021, 51(6): 84-92. |
|